import os
import requests
import pandas as pd
import numpy as np
import seaborn as sns
import matplotlib.pyplot as plt
import matplotlib.colors as mc
import plotly.express as px
import plotly.io as pio
import plotly.graph_objects as go
pio.renderers.default = 'notebook'

%matplotlib inline

# colors = ["#e21c34", "#500b28"]
# custom_cmap = mc.LinearSegmentedColormap.from_list("custom_gradient", colors)

# palette = custom_cmap(np.linspace(0.3, 1, 12))
color = "#f1faee"  # проставляем цвет фона - АКТУАЛЬНЫЙ f1faee
palette = plt.cm.OrRd(
    np.linspace(0.5, 1.0, 12)
)  # настраиваем палитру OrRd, убирая самые светлые оттенки
translate_into_hex = [
    f"#{int(r*255):02x}{int(g*255):02x}{int(b*255):02x}" for r, g, b, a in palette
]  # переводим палитру в HEX формат для применения в настройках Matplotlib, Seaborn, Plotly


plt.rcParams.update(
    {
        "font.family": "Arial",
        "font.size": 12,
        "figure.figsize": (14, 6),
        "figure.facecolor": f"{color}",
        "axes.facecolor": f"{color}",
        "grid.color": "#f0f0f0",
        "axes.prop_cycle": plt.cycler(color=translate_into_hex),
    }
)

sns.set_theme(
    style="whitegrid",
    palette=translate_into_hex,
    font="Arial",
    font_scale=1.0,
    rc={
        "figure.figsize": (14, 6),
        "figure.facecolor": f"{color}",
        "axes.facecolor": f"{color}",
        "grid.color": "#f0f0f0",
    },
)

custom_template = go.layout.Template()
custom_template.layout.colorway = translate_into_hex
custom_template.layout.font = {"family": "Arial", "size": 12}
custom_template.layout.title = {"x": 0.5, "font": {"size": 16, "weight": "bold"}}
custom_template.layout.paper_bgcolor = f"{color}"
custom_template.layout.plot_bgcolor = f"{color}"


pio.templates["my_custom"] = custom_template
pio.templates.default = "my_custom"

FILE_ID = "1PMhtD3LqyCzlZMEh-8aDPxre0wPw8v0U"  # ID файла на Google Drive
file_url = f"https://drive.google.com/uc?id={FILE_ID}"

pd.set_option("display.max_columns", 20)  # убирает ограничения отображения dataset
pd.set_option("display.max_rows", 150)

data_dir = "../data"  # создаем директорию data в директории notebooks.
if not os.path.exists(data_dir):
    os.makedirs(data_dir)

data_path = os.path.join(data_dir, "data_car.xlsx")  # путь до файла dataset

if os.path.isfile(data_path):  # если файл есть в data - читаем, если нет - скачиваем
    df = pd.read_excel(data_path)
    df = df.replace("-", pd.NA)  # убираем все ложные пропуски на NaN
    print(f"{'-'*60}")
    print("Dataset прочитан из директории data...")
    print(f"{'-'*60}")
else:
    response = requests.get(file_url)
    f_path = os.path.join(data_dir, "data_car.xlsx")  # путь до файла dataset
    with open(f_path, "wb") as f:
        f.write(response.content)

    df = pd.read_excel(f_path)
    df = df.replace("-", pd.NA)  # убираем все ложные пропуски на NaN
    print(f"{'-'*60}")
    print("Dataset загружен в директорию data и прочитан...")
    print(f"{'-'*60}")

------------------------------------------------------------
Dataset прочитан из директории data...
------------------------------------------------------------

df.head(10)

df.tail(10)

df.shape

(19237, 18)

df.dtypes

ID                    int64
Price                 int64
Levy                 object
Manufacturer         object
Model                object
Prod. year            int64
Category             object
Leather interior     object
Fuel type            object
Engine volume        object
Mileage              object
Cylinders           float64
Gear box type        object
Drive wheels         object
Doors                object
Wheel                object
Color                object
Airbags               int64
dtype: object

df = df.drop(["Cylinders", "Drive wheels", "Color", "Airbags"], axis=1)
df.head(5)

df.select_dtypes(exclude="number").head()

df.isna().sum()

ID                     0
Price                  0
Levy                5819
Manufacturer           0
Model                  0
Prod. year             0
Category               0
Leather interior       0
Fuel type              0
Engine volume          0
Mileage                0
Gear box type          0
Doors                  0
Wheel                  0
dtype: int64

plt.figure(figsize=(12, 6))
plt.imshow(df.isna(), aspect="auto", interpolation="nearest", cmap="gray")
plt.title("Visual display of passes\n", fontsize=16, fontweight="bold")
plt.xlabel("Column Number")
plt.ylabel("Sample Number")

Text(0, 0.5, 'Sample Number')

df["Levy"] = df["Levy"].astype(
    "Int16"
)  # данный тип данных позволяет преобразовать признак с пропусками
df["Levy"] = df["Levy"].fillna(df["Levy"].median())
df.isna().sum()

ID                  0
Price               0
Levy                0
Manufacturer        0
Model               0
Prod. year          0
Category            0
Leather interior    0
Fuel type           0
Engine volume       0
Mileage             0
Gear box type       0
Doors               0
Wheel               0
dtype: int64

df["Mileage"] = df["Mileage"].str.replace(" km", "", regex=False)
df["Mileage"].head(10)

0    186005
1    192000
2    200000
3    168966
4     91901
5    160931
6    258909
7    216118
8    398069
9    128500
Name: Mileage, dtype: object

df["Mileage"] = df["Mileage"].astype("int32")
df["Mileage"].head(10)

0    186005
1    192000
2    200000
3    168966
4     91901
5    160931
6    258909
7    216118
8    398069
9    128500
Name: Mileage, dtype: int32

df.select_dtypes(exclude="number").head()

df.select_dtypes(include="number").head()

uniq_val = df.select_dtypes(include="number").nunique().sort_values()

fig = px.bar(
    x=uniq_val.index,
    y=uniq_val.values,
    labels={"x": "Features", "y": "Count"},
    # template="seaborn",
)

fig.update_layout(
    xaxis_tickangle=0,
    showlegend=False,
    title="Unique values in each feature\n",
)

fig.update_traces(texttemplate="%{y}", textposition="outside")

fig.show()

df.shape

(19237, 14)

duplic_rows_df = df[df.duplicated()]
duplic_rows_df.shape

(313, 14)

df = df.drop_duplicates()
df.shape

(18924, 14)

df.head()

df = df.rename(
    columns={
        "Levy": "Tax",
        "Prod. year": "Release_year",
        "Category": "Car_type",
        "Gear box type": "Transmission_type",
    }
)
df.head()

df.select_dtypes(include="number").head()

plt.figure(figsize=(12, 6))
sns.boxplot(x=df["Price"])
plt.title("Emissions in <Price>\n", fontsize=16, fontweight="bold")
plt.show()

plt.figure(figsize=(12, 6))
sns.boxplot(x=df["Tax"])
plt.title("Emissions in <Tax>\n", fontsize=16, fontweight="bold")
plt.show()

plt.figure(figsize=(12, 6))
sns.boxplot(x=df["Release_year"])
plt.title("Emissions in <Release_year>\n", fontsize=16, fontweight="bold")
plt.show()

plt.figure(figsize=(12, 6))
sns.boxplot(x=df["Mileage"])
plt.title("Emissions in <Mileage>\n", fontsize=16, fontweight="bold")
plt.show()

df["Price"].nlargest(5)

16983    26307500
8541       872946
1225       627220
5008       308906
9367       297930
Name: Price, dtype: int64

df[df["Price"].isin([26307500, 872946, 627220])]

df["Price"] = df["Price"].mask(df["Price"] > 900000, df["Price"].median())
df["Price"].nlargest(5)

8541     872946
1225     627220
5008     308906
9367     297930
14839    297930
Name: Price, dtype: int64

plt.figure(figsize=(12, 6))
sns.boxplot(x=df["Price"])
plt.title("Emissions in <Price> after median\n", fontsize=16, fontweight="bold")
plt.show()

df["Tax"].nlargest(10)

115      11714
18984    11714
18957    11706
17117     7536
3994      7063
2159      7058
5529      5908
5367      5877
17767     5681
2323      5679
Name: Tax, dtype: Int16

df[df["Tax"].isin([11714, 11706, 7536, 7063, 7058])]

df["Mileage"].nlargest(10)

2278     2147483647
6157     2147483647
11901    2147483647
12734    2147483647
15347    2147483647
15393    2147483647
19167    2147483647
17582    1777777778
7724     1234567899
9524     1111111111
Name: Mileage, dtype: int32

Q1_Mil = df["Mileage"].quantile(0.25)
Q3_Mil = df["Mileage"].quantile(0.75)
IQR_Mil = Q3_Mil - Q1_Mil
up_bound_Mil = Q3_Mil + 1.5 * IQR_Mil
down_bound_Mil = Q1_Mil - 1.5 * IQR_Mil

errors = df[df["Mileage"] > up_bound_Mil]
errors_type_2 = df[df["Mileage"] < down_bound_Mil]
len(errors)  # количество записей-выбросов выше верхней границы

635

len(errors_type_2)  # количество записей-выбросов ниже нижней границы

0

df = df[
    df["Mileage"] <= up_bound_Mil
]  # оставляем в Mileage только те значения, которые попали до верхней границы
df["Mileage"].nlargest(10)

1086     367053
7259     367000
14709    367000
4961     366869
8948     366869
9125     366869
10944    366869
15132    366869
16628    366869
9840     365810
Name: Mileage, dtype: int32

plt.figure(figsize=(12, 6))
sns.boxplot(x=df["Mileage"])
plt.title("Emissions in <Mileage> after delete\n", fontsize=16, fontweight="bold")
plt.show()

df.dtypes

ID                    int64
Price                 int64
Tax                   Int16
Manufacturer         object
Model                object
Release_year          int64
Car_type             object
Leather interior     object
Fuel type            object
Engine volume        object
Mileage               int32
Transmission_type    object
Doors                object
Wheel                object
dtype: object

df["ID"] = df["ID"].astype("int32")
df["Price"] = df["Price"].astype("int32")
df["Manufacturer"] = df["Manufacturer"].astype("category")
df["Model"] = df["Model"].astype("category")
df["Tax"] = df["Tax"].astype("int32")
df["Release_year"] = df["Release_year"].astype("int16")
df["Car_type"] = df["Car_type"].astype("category")
df["Have a leather interior?"] = (
    df["Leather interior"].map({"No": 0, "Yes": 1}).astype("bool")
)  # добавляем к оригинальному признаку  булевый признак.

df["Car have a left wheel?"] = (
    df["Wheel"].map({"Right-hand drive": 0, "Left wheel": 1}).astype("bool")
)

df["Fuel type"] = df["Fuel type"].astype("category")
df["Transmission_type"] = df["Transmission_type"].astype("category")
df["Doors"] = df["Doors"].astype("category")
df[["Leather interior", "Wheel"]] = df[
    ["Have a leather interior?", "Car have a left wheel?"]
]  # заменяем оригинал на булевый.
df = df.drop(
    columns=["Have a leather interior?", "Car have a left wheel?"]
)  # удаляем дополнительные признаки.

df.rename(
    columns={"Leather interior": "Have a leather interior?"}, inplace=True
)  # переименовываем.
df.rename(columns={"Wheel": "Car have a left wheel?"}, inplace=True)

df.dtypes

ID                             int32
Price                          int32
Tax                            int32
Manufacturer                category
Model                       category
Release_year                   int16
Car_type                    category
Have a leather interior?        bool
Fuel type                   category
Engine volume                 object
Mileage                        int32
Transmission_type           category
Doors                       category
Car have a left wheel?          bool
dtype: object

fi_path = os.path.join(data_dir, "data_car.parquet")  # путь до файла dataset
df.to_parquet(fi_path)  # сохраняем dataset в .parquet
df_par = pd.read_parquet(fi_path)
df_par.head()

completeness = df_par.count() / len(df_par)
uniqueness = df_par["ID"].nunique() / len(
    df_par["ID"]
)  # так как этот параметр напрямую отвечает за уникальность значений в признаках.
print(completeness)
print(f"{'-'*60}")
print(f"ID = {uniqueness}")

ID                          1.0
Price                       1.0
Tax                         1.0
Manufacturer                1.0
Model                       1.0
Release_year                1.0
Car_type                    1.0
Have a leather interior?    1.0
Fuel type                   1.0
Engine volume               1.0
Mileage                     1.0
Transmission_type           1.0
Doors                       1.0
Car have a left wheel?      1.0
dtype: float64
------------------------------------------------------------
ID = 1.0

try:
    assert (df_par["Price"].dtype == "int32") and ((df_par["Price"] >= 0).all())
    print("Валидация Price - успешна")
except AssertionError:
    print("ERROR in Price")

try:
    assert (df_par["Tax"].dtype == "int32") and ((df_par["Tax"] >= 0).all())
    print("Валидация Tax - успешна")
except AssertionError:
    print("ERROR in Tax")

try:
    assert (
        (df_par["Release_year"].dtype == "int16")
        and ((df_par["Release_year"] >= 1900).all())
        and ((df_par["Release_year"] <= 2100).all())
    )
    print("Валидация Release_year - успешна")
except AssertionError:
    print("ERROR in Release_year")

try:
    assert (df_par["Have a leather interior?"].dtype == "bool") and (
        (df_par["Have a leather interior?"].isin([True, False]).all())
    )
    print("Валидация Have a leather interior? - успешна")
except AssertionError:
    print("ERROR in Have a leather interior?")

try:
    assert (df_par["Mileage"].dtype == "int32") and ((df_par["Mileage"] >= 0).all())
    print("Валидация Mileage - успешна")
except AssertionError:
    print("ERROR in Mileage")

try:
    assert (df_par["Car have a left wheel?"].dtype == "bool") and (
        (df_par["Car have a left wheel?"].isin([True, False]).all())
    )
    print("Валидация Car have a left wheel? - успешна")
except AssertionError:
    print("ERROR in Car have a left wheel?")

Валидация Price - успешна
Валидация Tax - успешна
Валидация Release_year - успешна
Валидация Have a leather interior? - успешна
Валидация Mileage - успешна
Валидация Car have a left wheel? - успешна

plt.figure(figsize=(12, 6))
sns.boxplot(x=df["Price"])
plt.title("Emissions in <Price>\n", fontsize=16, fontweight="bold")
plt.show()

fig = px.histogram(
    df_par,
    x="Price",
    nbins=50,
    # template="seaborn",
)

fig.update_layout(
    xaxis_tickangle=0,
    showlegend=False,
    title="Price distribution",
)

fig.show()

num_df_par = df_par.select_dtypes(include="number").columns

plt.figure(figsize=(10, 8))
sns.heatmap(df_par[num_df_par].corr(), annot=True, cmap="OrRd")
plt.title("The correlation matrix\n", fontsize=16, fontweight="bold")
plt.show()

top_corr_ft = (
    df_par[num_df_par].corr()["Price"].abs().sort_values(ascending=False).index[1:4]
)
for features in top_corr_ft:
    sns.scatterplot(data=df_par, x=features, y="Price")
    plt.title(f"<Price> vs <{features}>\n", fontsize=16, fontweight="bold")
    plt.show()

num_cols = ["Release_year", "Mileage", "Tax"]  # числовые признаки

fig, axes = plt.subplots(2, 2, figsize=(12, 6))
fig.suptitle("Distribution of numerical features\n", fontsize=16, fontweight="bold")

axes_flat = axes.flat  # преобразование массива 2х2 в массив 1:4 для перебора элементов

for i, col in enumerate(num_cols):
    sns.histplot(data=df_par, x=col, ax=axes_flat[i], bins=50, kde=True)
    axes_flat[i].set_title(f"Distribution <{col}>\n")
    axes_flat[i].tick_params(axis="x", rotation=0)

for i in range(len(num_cols), len(axes_flat)):
    axes_flat[i].set_visible(False)

plt.tight_layout()
plt.show()

# включаем шрифт поддерживающий грузинские буквы, так как в таблице есть такие автомобили
plt.rcParams["font.family"] = "DejaVu Sans"
plt.rcParams["font.sans-serif"] = ["DejaVu Sans", "Arial Unicode MS", "Liberation Sans"]

cat_ft = df_par.select_dtypes(exclude="number").columns

for features in cat_ft:
    n_cat = df_par[features].nunique()  # количество уникальных значений в признаке

    plt.figure(figsize=(25, 10))
    sns.boxplot(data=df_par, x=features, y="Price")
    plt.xticks(rotation=90)
    plt.title(f"<Price> vs <{features}>\n", fontsize=16, fontweight="bold")
    plt.tight_layout()
    plt.show()

plt.rcParams["font.family"] = "sans-serif"
plt.rcParams["font.sans-serif"] = ["Arial", "DejaVu Sans", "Liberation Sans"]

from sklearn.ensemble import RandomForestRegressor


ft = [
    col for col in df_par.columns if col not in ["Price", "ID"]
]  # исключаем ID и Price как признаки из анализа

X = df_par[ft].copy()  # на ось X ставятся наши признаки
y = df_par["Price"]

X_encoded = X.copy()
for col in X_encoded.select_dtypes(exclude="number").columns:
    X_encoded[col] = (
        X_encoded[col].astype("category").cat.codes
    )  # перевод категориальных признаков в числа

model = RandomForestRegressor(
    random_state=10, n_estimators=100
)  # cама модель, где n_estimators - количество деревьев решений(каждое анализирует по-своему), random_state - только для воспроизводимости результатов (может быть любым)
model.fit(X_encoded, y)

feature_importance = pd.DataFrame(
    {"feature": ft, "importance": model.feature_importances_}
).sort_values(
    "importance", ascending=False
)  # сортировка по важности

plt.figure(figsize=(12, 6))
sns.barplot(
    data=feature_importance.head(12),
    x="importance",
    y="feature",
    hue="feature",
    palette="OrRd_r",
    legend=False,
)
plt.title("Top 12 significant features for car prices", fontsize=16, fontweight="bold")
plt.xlabel(
    "The importance of the feature",
)
plt.ylabel("")
plt.tight_layout()
plt.show()

# Выводим таблицу с результатами
print("Ranking of features by importance:")
print("=" * 50)
for i, row in feature_importance.iterrows():
    print(f"{i+1:2d}. {row['feature']:25} | importance: {row['importance']:.4f}")

Ranking of features by importance:
==================================================
 8. Engine volume             | importance: 0.2355
 4. Release_year              | importance: 0.2153
 9. Mileage                   | importance: 0.1403
 3. Model                     | importance: 0.0956
 1. Tax                       | importance: 0.0767
10. Transmission_type         | importance: 0.0659
 7. Fuel type                 | importance: 0.0588
 5. Car_type                  | importance: 0.0471
 2. Manufacturer              | importance: 0.0436
 6. Have a leather interior?  | importance: 0.0125
11. Doors                     | importance: 0.0067
12. Car have a left wheel?    | importance: 0.0020

if len(top_corr_ft) >= 2:
    fig = px.scatter_3d(
        df_par,
        x=top_corr_ft[0],
        y=top_corr_ft[1],
        z="Price",
        color="Price",
        title=f"3D scatter: <{top_corr_ft[0]}> vs <{top_corr_ft[1]}> vs <Price>",
        # template="seaborn",
        color_continuous_scale="OrRd_r",
    )
    fig.show()

	ID	Price	Levy	Manufacturer	Model	Prod. year	Category	Leather interior	Fuel type	Engine volume	Mileage	Cylinders	Gear box type	Drive wheels	Doors	Wheel	Color	Airbags
0	45654403	13328	1399	LEXUS	RX 450	2010	Jeep	Yes	Hybrid	3.5	186005 km	6.0	Automatic	4x4	04-May	Left wheel	Silver	12
1	44731507	16621	1018	CHEVROLET	Equinox	2011	Jeep	No	Petrol	3	192000 km	6.0	Tiptronic	4x4	04-May	Left wheel	Black	8
2	45774419	8467	<NA>	HONDA	FIT	2006	Hatchback	No	Petrol	1.3	200000 km	4.0	Variator	Front	04-May	Right-hand drive	Black	2
3	45769185	3607	862	FORD	Escape	2011	Jeep	Yes	Hybrid	2.5	168966 km	4.0	Automatic	4x4	04-May	Left wheel	White	0
4	45809263	11726	446	HONDA	FIT	2014	Hatchback	Yes	Petrol	1.3	91901 km	4.0	Automatic	Front	04-May	Left wheel	Silver	4
5	45802912	39493	891	HYUNDAI	Santa FE	2016	Jeep	Yes	Diesel	2	160931 km	4.0	Automatic	Front	04-May	Left wheel	White	4
6	45656768	1803	761	TOYOTA	Prius	2010	Hatchback	Yes	Hybrid	1.8	258909 km	4.0	Automatic	Front	04-May	Left wheel	White	12
7	45816158	549	751	HYUNDAI	Sonata	2013	Sedan	Yes	Petrol	2.4	216118 km	4.0	Automatic	Front	04-May	Left wheel	Grey	12
8	45641395	1098	394	TOYOTA	Camry	2014	Sedan	Yes	Hybrid	2.5	398069 km	4.0	Automatic	Front	04-May	Left wheel	Black	12
9	45756839	26657	<NA>	LEXUS	RX 350	2007	Jeep	Yes	Petrol	3.5	128500 km	6.0	Automatic	4x4	04-May	Left wheel	Silver	12

	ID	Price	Levy	Manufacturer	Model	Prod. year	Category	Leather interior	Fuel type	Engine volume	Mileage	Cylinders	Gear box type	Drive wheels	Doors	Wheel	Color	Airbags
19227	45769427	29793	1053	MERCEDES-BENZ	E 350	2014	Sedan	Yes	Diesel	3.5	219030 km	6.0	Automatic	4x4	04-May	Left wheel	Black	12
19228	45773726	706	1850	MERCEDES-BENZ	E 350	2008	Sedan	Yes	Diesel	3.5	122874 km	6.0	Automatic	Rear	04-May	Left wheel	Black	12
19229	39977395	50	<NA>	TOYOTA	Prius	2008	Hatchback	No	Hybrid	1.5	150000 km	4.0	Automatic	Front	04-May	Left wheel	Silver	6
19230	45760891	470	645	TOYOTA	Prius	2011	Hatchback	Yes	Hybrid	1.8	307325 km	4.0	Automatic	Front	04-May	Left wheel	Silver	12
19231	45772306	5802	1055	MERCEDES-BENZ	E 350	2013	Sedan	Yes	Diesel	3.5	107800 km	6.0	Automatic	Rear	04-May	Left wheel	Grey	12
19232	45798355	8467	<NA>	MERCEDES-BENZ	CLK 200	1999	Coupe	Yes	CNG	2.0 Turbo	300000 km	4.0	Manual	Rear	02-Mar	Left wheel	Silver	5
19233	45778856	15681	831	HYUNDAI	Sonata	2011	Sedan	Yes	Petrol	2.4	161600 km	4.0	Tiptronic	Front	04-May	Left wheel	Red	8
19234	45804997	26108	836	HYUNDAI	Tucson	2010	Jeep	Yes	Diesel	2	116365 km	4.0	Automatic	Front	04-May	Left wheel	Grey	4
19235	45793526	5331	1288	CHEVROLET	Captiva	2007	Jeep	Yes	Diesel	2	51258 km	4.0	Automatic	Front	04-May	Left wheel	Black	4
19236	45813273	470	753	HYUNDAI	Sonata	2012	Sedan	Yes	Hybrid	2.4	186923 km	4.0	Automatic	Front	04-May	Left wheel	White	12

	ID	Price	Levy	Manufacturer	Model	Prod. year	Category	Leather interior	Fuel type	Engine volume	Mileage	Gear box type	Doors	Wheel
0	45654403	13328	1399	LEXUS	RX 450	2010	Jeep	Yes	Hybrid	3.5	186005 km	Automatic	04-May	Left wheel
1	44731507	16621	1018	CHEVROLET	Equinox	2011	Jeep	No	Petrol	3	192000 km	Tiptronic	04-May	Left wheel
2	45774419	8467	<NA>	HONDA	FIT	2006	Hatchback	No	Petrol	1.3	200000 km	Variator	04-May	Right-hand drive
3	45769185	3607	862	FORD	Escape	2011	Jeep	Yes	Hybrid	2.5	168966 km	Automatic	04-May	Left wheel
4	45809263	11726	446	HONDA	FIT	2014	Hatchback	Yes	Petrol	1.3	91901 km	Automatic	04-May	Left wheel

	Levy	Manufacturer	Model	Category	Leather interior	Fuel type	Engine volume	Mileage	Gear box type	Doors	Wheel
0	1399	LEXUS	RX 450	Jeep	Yes	Hybrid	3.5	186005 km	Automatic	04-May	Left wheel
1	1018	CHEVROLET	Equinox	Jeep	No	Petrol	3	192000 km	Tiptronic	04-May	Left wheel
2	<NA>	HONDA	FIT	Hatchback	No	Petrol	1.3	200000 km	Variator	04-May	Right-hand drive
3	862	FORD	Escape	Jeep	Yes	Hybrid	2.5	168966 km	Automatic	04-May	Left wheel
4	446	HONDA	FIT	Hatchback	Yes	Petrol	1.3	91901 km	Automatic	04-May	Left wheel

	Manufacturer	Model	Category	Leather interior	Fuel type	Engine volume	Gear box type	Doors	Wheel
0	LEXUS	RX 450	Jeep	Yes	Hybrid	3.5	Automatic	04-May	Left wheel
1	CHEVROLET	Equinox	Jeep	No	Petrol	3	Tiptronic	04-May	Left wheel
2	HONDA	FIT	Hatchback	No	Petrol	1.3	Variator	04-May	Right-hand drive
3	FORD	Escape	Jeep	Yes	Hybrid	2.5	Automatic	04-May	Left wheel
4	HONDA	FIT	Hatchback	Yes	Petrol	1.3	Automatic	04-May	Left wheel

Car Price Prediction - Exploratory data analysis.

Вступление¶

Информация об dataset.¶

Цель (target) EDA.¶

Этапы EDA.¶

1) Загрузка данных с сайта и сохранения их в директории для дальнейшего взаимодействия¶

2) Первичный осмотр данных¶

3) Операции над данными.¶

3.1) Удаление признаков¶

3.2) Проверка категориальных признаков¶

3.3) Проверка числовых признаков¶

3.4) Проверка дубликатов¶

3.5) Переименование признаков¶

3.6) Обнаружение и обработка выбросов в числовых признаках¶

3.7) Приведение типов и сохранение в формате .parquet¶

4) Проверка по метрикам¶

5) Валидация данных¶

6) Анализы данных для решения задачи¶

6.1) Анализ распределения целевой переменной - Price¶

6.2) Построение корреляционной карты и точечных графиков для коррелирующих признаков¶

7) Итоги¶

	ID	Price	Tax	Manufacturer	Model	Release_year	Car_type	Leather interior	Fuel type	Engine volume	Mileage	Transmission_type	Doors	Wheel
1225	45795524	627220	781	MERCEDES-BENZ	G 65 AMG 63AMG	2020	Jeep	Yes	Petrol	6.3 Turbo	0	Tiptronic	04-May	Left wheel
8541	45761204	872946	2067	LAMBORGHINI	Urus	2019	Universal	Yes	Petrol	4	2531	Tiptronic	04-May	Left wheel
16983	45812886	26307500	781	OPEL	Combo	1999	Goods wagon	No	Diesel	1.7	99999	Manual	02-Mar	Left wheel

	ID	Price	Tax	Manufacturer	Model	Release_year	Car_type	Leather interior	Fuel type	Engine volume	Mileage	Transmission_type	Doors	Wheel
115	45534351	11917	11714	MERCEDES-BENZ	E 500 AMG	2003	Sedan	Yes	Petrol	5	150000	Tiptronic	04-May	Right-hand drive
2159	45804871	10349	7058	SUBARU	Legacy	2005	Sedan	Yes	Petrol	3	147000	Tiptronic	04-May	Right-hand drive
3994	45782188	13172	7063	TOYOTA	Alphard	2003	Minivan	Yes	LPG	3	190000	Automatic	04-May	Right-hand drive
17117	45781442	7213	7536	MITSUBISHI	Pajero	2000	Jeep	Yes	CNG	3.2	210000	Automatic	04-May	Right-hand drive
18957	44674964	14740	11706	MERCEDES-BENZ	E 500 AVG	2005	Sedan	Yes	Petrol	5	56000	Tiptronic	04-May	Right-hand drive
18984	45221191	11917	11714	MERCEDES-BENZ	E 500	2003	Sedan	Yes	Petrol	5	150000	Tiptronic	04-May	Right-hand drive